Clustering and LLM Integration for SEO

Discovering interlinking opportunities is crucial for enhancing a website's SEO performance. This innovative method combines K-means clustering, a classic machine learning technique, with the power of large language models (LLMs) to identify semantically related web pages for internal linking. By leveraging Python and advanced AI tools, this approach offers a more nuanced and context-aware solution than traditional clustering methods alone.

The process begins by converting web page titles into numerical vectors using OpenAI's embedding model, capturing the semantic essence of each page. These embeddings are then clustered using the K-means algorithm, grouping similar pages together. What sets this method apart is the integration of LLMs at two critical stages: First, an LLM evaluates the quality of each cluster, providing a human-like assessment of how well the pages fit together. Second, it generates meaningful names for each cluster, offering valuable context for SEO professionals.

This hybrid approach iterates through different numbers of clusters, combining traditional mathematical metrics like inertia and silhouette scores with the LLM-based quality assessments. The result is an optimized clustering that balances computational efficiency with semantic relevance. By using an LLM as a proxy for human judgment, this method can automatically identify interlinking opportunities that are not just mathematically similar, but contextually meaningful, potentially leading to more effective internal linking strategies and improved SEO outcomes.